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Enable Mask R-CNN with Inference Engine. Full coverage with nGraph

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This commit is contained in:
Dmitry Kurtaev
2020-02-16 22:12:14 +03:00
parent a6f3a21256
commit f3eef792eb
5 changed files with 118 additions and 32 deletions
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modules/dnn/test/test_tf_importer.cpp
+27 -4
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@@ -914,8 +914,16 @@ TEST(Test_TensorFlow, two_inputs)
normAssert(out, firstInput + secondInput);
}
TEST(Test_TensorFlow, Mask_RCNN)
TEST_P(Test_TensorFlow_nets, Mask_RCNN)
{
static const double kMaskThreshold = 0.5;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
applyTestTag(CV_TEST_TAG_MEMORY_1GB, CV_TEST_TAG_DEBUG_VERYLONG);
Mat img = imread(findDataFile("dnn/street.png"));
std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt");
@@ -926,7 +934,8 @@ TEST(Test_TensorFlow, Mask_RCNN)
Mat refMasks = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_masks.npy"));
Mat blob = blobFromImage(img, 1.0f, Size(800, 800), Scalar(), true, false);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(blob);
@@ -940,7 +949,10 @@ TEST(Test_TensorFlow, Mask_RCNN)
Mat outDetections = outs[0];
Mat outMasks = outs[1];
normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.019 : 2e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.018 : default_lInf;
normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5, scoreDiff, iouDiff);
// Output size of masks is NxCxHxW where
// N - number of detected boxes
@@ -964,7 +976,18 @@ TEST(Test_TensorFlow, Mask_RCNN)
outMasks(srcRanges).copyTo(masks(dstRanges));
}
cv::Range topRefMasks[] = {Range::all(), Range(0, numDetections), Range::all(), Range::all()};
normAssert(masks, refMasks(&topRefMasks[0]));
refMasks = refMasks(&topRefMasks[0]);
// make binary masks
cv::threshold(masks.reshape(1, 1), masks, kMaskThreshold, 1, THRESH_BINARY);
cv::threshold(refMasks.reshape(1, 1), refMasks, kMaskThreshold, 1, THRESH_BINARY);
double inter = cv::countNonZero(masks & refMasks);
double area = cv::countNonZero(masks | refMasks);
EXPECT_GE(inter / area, 0.99);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
expectNoFallbacks(net);
}
}